An overview of robotics and autonomous systems for harsh environments

Across a wide range of industries and applications, robotics and autonomous systems can fulfil the crucial and challenging tasks such as inspection, exploration, monitoring, drilling, sampling and mapping in areas of scientific discovery, disaster prevention, human rescue and infrastructure management, etc. However, in many situations, the associated environment is either too dangerous or inaccessible to humans. Hence, a wide range of robots have been developed and deployed to replace or aid humans in these activities. A look at these harsh environment applications of robotics demonstrate the diversity of technologies developed. This paper reviews some key application areas of robotics that involve interactions with harsh environments (such as search and rescue, space exploration, and deep-sea operations), gives an overview of the developed technologies and provides a discussion of the key trends and future directions common to many of these areas

Development Of A Vision System For Ship Hull Inspection

Penyelidikan ini memperkenalkan strategi pengawalan untuk memperbaiki prestasi pemeriksaan visual badan kapal dengan menggunakan kenderaan dalam air. This work introduces a strategy to improve the performance of visual ship hull inspection using a Remotely-Operated Vehicle (ROV) as its underwater vehicle platform

Context-Enabled Visualization Strategies for Automation Enabled Human-in-the-loop Inspection Systems to Enhance the Situation Awareness of Windstorm Risk Engineers

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. Traditionally, the risk inspection process is highly subjective and depends on the skills of the engineer performing it. This dissertation investigates the sensemaking process of risk engineers while performing risk inspection with special focus on various factors influencing it. This research then investigates how context-based visualizations strategies enhance the situation awareness and performance of windstorm risk engineers. An initial study investigated the sensemaking process and situation awareness requirements of the windstorm risk engineers. The data frame theory of sensemaking was used as the framework to carry out this study. Ten windstorm risk engineers were interviewed, and the data collected were analyzed following an inductive thematic approach. The themes emerged from the data explained the sensemaking process of risk engineers, the process of making sense of contradicting information, importance of their experience level, internal and external biases influencing the inspection process, difficulty developing mental models, and potential technology interventions. More recently human in the loop systems such as drones have been used to improve the efficiency of windstorm risk inspection. This study provides recommendations to guide the design of such systems to support the sensemaking process and situation awareness of windstorm visual risk inspection. The second study investigated the effect of context-based visualization strategies to enhance the situation awareness of the windstorm risk engineers. More specifically, the study investigated how different types of information contribute towards the three levels of situation awareness. Following a between subjects study design 65 civil/construction engineering students completed this study. A checklist based and predictive display based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of windstorm risk engineers. However, the predictive display only helped with certain tasks like understanding the interaction among different components on the rooftop. For remaining tasks, checklist alone was sufficient. Moreover, the decision aids did not place any additional cognitive demand on the participants. This study helped us understand the advantages and disadvantages of the decision aids tested. The final study evaluated the transfer of training effect of the checklist and predictive display based decision aids. After one week of the previous study, participants completed a follow-up study without any decision aids. The performance and situation awareness of participants in the checklist and predictive display group did not change significantly from first trial to second trial. However, the performance and situation awareness of participants in the control condition improved significantly in the second trial. They attributed this to their exposure to SAGAT questionnaire in the first study. They knew what issues to look for and what tasks need to be completed in the simulation. The confounding effect of SAGAT questionnaires needs to be studied in future research efforts

Development Of A Vision System For Ship Hull Inspection [K4165. Z94 2007 f rb].

Penyelidikan ini memperkenalkan strategi pengawalan untuk memperbaiki prestasi pemeriksaan visual badan kapal dengan menggunakan kenderaan dalam air. Kaedah yang dicadangkan bertujuan untuk membangunkan sebuah sistem yang secara visualnya sentiasa kekal selari pada permukaan badan kapal. This work introduces a strategy to improve the performance of visual ship hull inspection using a Remotely-Operated Vehicle (ROV) as its underwater vehicle platform. The proposed method is aimed at developing a system that will maintain the camera viewing angle parallel to the ship hull surface

Infrastructure robotics: Research challenges and opportunities

Infrastructure robotics is about research on and development of methodologies that enable robotic systems to be used in civil infrastructure inspection, maintenance and rehabilitation. This paper briefly discusses the current research challenges and opportunities in infrastructure robotics, and presents a review of the research activities and projects in this field at the Centre for Autonomous Systems, University of Technology Sydney

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

Position error estimation of the underwater coordinate measurement machine using artificial neural network

Position error of the underwater coordinate measurement machine (UWCMM) is estimated using an artificial neural network (ANN). The UWCMM has a multi joint serial link and the position of an underwater robot attached to the tip is obtained from kinematics of the link. However, the inaccuracy of the kinematics model creates a position error. In order to improve measurement accuracy, an ANN model which estimate position error of the kinematics of the UWCMM using joint angles data is constructed. The ANN model was trained by 462 data and verified by 42 data. The error estimation result of the trained ANN reduces maximally 66.5 % of the average of the position error of the verification data

Task priority control of underwater intervention systems: Theory and applications

This paper presents a unifying task priority control architecture for underwater vehicle manipulator systems. The proposed control framework can be applied to different operative scenarios such as waypoint navigation, assisted teleoperation, interaction, landing and grasping. This work extends the results of the TRIDENT and MARIS projects, which were limited to the execution of grasping actions, to other applications taken from the DexROV and ROBUST projects. In particular, simulation results show how the control framework can be used, for example, for pipeline inspection scenarios and deep sea mining exploration